Bisphenol A increases aP2 expression in 3T3L1 by enhancing the transcriptional activity of nuclear receptors at the promoter
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Environmental pollutants, such as bisphenol A (BPA), have the potential to affect the differentiation processes and the biology of the adipose tissue. The 3T3-L1 model is one of the murine cell models used extensively for the investigation of the molecular events that govern the differentiation of adipocytes from a committed preadipocyte to a mature, lipid laden adipocyte. Most of the studies investigating the effects of BPA on preadipocyte differentiation have investigated the effects of this chemical in the presence of an optimal differentiation cocktail containing high concentrations of the synthetic glucocorticoid dexamethasone, conditions that result in 90% to 100% of differentiated adipocytes. Our studies employed the 3T3-L1 cell model in the absence of exogenous glucocorticoids. We show that BPA is able to increase the differentiation of the 3T3-L1 cells under these conditions. Furthermore, the effect of BPA was observed in the absence of the synthetic glucocorticoid (dexamethasone), a hormone known to be required for the differentiation of the 3T3-L1 cells. In addition, BPA upregulated the mRNA expression and protein levels of the terminal marker of adipogenesis the fatty acid binding protein (aP2) in these cells. Interestingly, the known modulators of adipogenesis such as the peroxisome proliferator-activated receptor (PPAR) γ or CCAAT enhancer binding protein (C/EBP) α were not elevated at the mRNA or protein level in response to BPA. Furthermore, BPA upregulated the expression levels of the marker of adipogenesis aP2, through an effect on the transcriptional activity of C/EBPδ and the glucocorticoid receptor (GR) at its promoter.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it